Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation
This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected fr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/20/9765 |
| _version_ | 1797515346312167424 |
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| author | Hui Xue Bjørn-Morten Batalden Puneet Sharma Jarle André Johansen Dilip K. Prasad |
| author_facet | Hui Xue Bjørn-Morten Batalden Puneet Sharma Jarle André Johansen Dilip K. Prasad |
| author_sort | Hui Xue |
| collection | DOAJ |
| description | This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies. |
| first_indexed | 2024-03-10T06:44:12Z |
| format | Article |
| id | doaj.art-447a9a8246604c0ba1b4d423ace3f0c2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:44:12Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-447a9a8246604c0ba1b4d423ace3f0c22023-11-22T17:24:02ZengMDPI AGApplied Sciences2076-34172021-10-011120976510.3390/app11209765Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime NavigationHui Xue0Bjørn-Morten Batalden1Puneet Sharma2Jarle André Johansen3Dilip K. Prasad4Department of Technology and Safety, UiT The Arctic University of Norway, 9019 Tromsø, NorwayDepartment of Technology and Safety, UiT The Arctic University of Norway, 9019 Tromsø, NorwayDepartment of Automation and Processing Technology, UiT The Arctic University of Norway, 9019 Tromsø, NorwayDepartment of Automation and Processing Technology, UiT The Arctic University of Norway, 9019 Tromsø, NorwayDepartment of Computer Science, UiT The Arctic University of Norway, 9019 Tromsø, NorwayThis work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.https://www.mdpi.com/2076-3417/11/20/9765biosignalmaritime navigationclassificationsituation awareness (SA)neural networkmaritime training |
| spellingShingle | Hui Xue Bjørn-Morten Batalden Puneet Sharma Jarle André Johansen Dilip K. Prasad Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation Applied Sciences biosignal maritime navigation classification situation awareness (SA) neural network maritime training |
| title | Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation |
| title_full | Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation |
| title_fullStr | Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation |
| title_full_unstemmed | Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation |
| title_short | Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation |
| title_sort | biosignal based driving skill classification using machine learning a case study of maritime navigation |
| topic | biosignal maritime navigation classification situation awareness (SA) neural network maritime training |
| url | https://www.mdpi.com/2076-3417/11/20/9765 |
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